Publications:Categorizing cells in phytoplankton images
From ISLAB/CAISR
| Title | Categorizing cells in phytoplankton images |
|---|---|
| Author | Adas Gelzinis and Antanas Verikas and Marija Bacauskiene and Irina Olenina and Sergej Olenin |
| Year | 2011 |
| PublicationType | Conference Paper |
| Journal | |
| HostPublication | Recent Advances in Signal Processing, Computational Geometry and Systems Theory |
| Conference | The 11th WSEAS International Conference on Signal Processing, Computational Geometry and Artificial Vision (ISCGAV'11), Florence, Italy, August 23-25, 2011 |
| DOI | |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:438554 |
| Abstract | This article is concerned with detection of invasive species---Prorocentrum minimum (P. minimum)---in phytoplankton images. The species is known to cause harmful blooms in many estuarine and coastal environments. A new technique, combining phase congruency-based detection of circular objects in images, stochastic optimization, image segmentation, and SVM and random forest-based classification of objects was developed to solve the task. The developed algorithms were tested using 114 images of 1280 x 960 pixels. There were 2088 P. minimum cells in the images in total. The algorithms were able to detect 93.25% of objects representing P. minimum cells and correctly classify 94.9% of all objects. The results are rather encouraging and will be used to develop an automated system for obtaining abundance estimates of the species. |
